AI Boilerplates

Explore 41 boilerplates in this collection. Find the perfect starting point for your next project.

Visit website for SaaSBold

SaaSBold

Full-stack, production ready Next.js SaaS boilerplate and starter kit

JavaScript
TypeScript
Tailwind CSS
PostgreSQL
Lemon Squeezy
Paddle
Stripe
Next.js
React

Features:

Admin
AI
Analytics
API
Auth
CRUD
i18n
+6 more
Visit website for Makerkit

Makerkit

A SaaS Starter Kit for building production-ready React applications

JavaScript
TypeScript
Lucide Icons
Radix UI
shadcn/ui
Tailwind CSS
Firestore
Supabase
Lemon Squeezy
Stripe
Next.js
React
React Native
Remix

Features:

2FA
Admin
AI
Analytics
Auth
Blog
Dark Mode
+16 more
Visit website for Nuxflare Pro

Nuxflare Pro

The Complete Nuxt + Cloudflare Starter Kit

JavaScript
TypeScript
Nuxt
Drizzle ORM
Paddle
Stripe
Nuxt
Pulumi
SST.dev
tRPC

Features:

Access Control
AI
Analytics
Auth
Background Jobs
Billing
Caching
+11 more
Visit website for Super SaaS

Super SaaS

The Simple, Fast & Smart Nuxt 3 Fullstack Kit

JavaScript
TypeScript
Nuxt UI
Radix Vue
shadcn/vue
Tailwind CSS
Drizzle ORM
Lemon Squeezy
Stripe
Nuxt

Features:

Admin
AI
API
Auth
Dark Mode
Emails
ORM
+6 more
Visit website for Petal

Petal

Tools to help you rapidly build Phoenix web applications without worrying about design or reinventing the wheel.

Elixir
HEEX
Tailwind CSS
Stripe
LiveView
Phoenix

Features:

Admin
AI
Auth
Charts
CRUD
Deployment
Emails
+7 more
Visit website for Staarter.dev

Staarter.dev

A comprehensive Next.js SaaS template with pre-configured authentication, billing, and localization

JavaScript
TypeScript
shadcn/ui
Tailwind CSS
MongoDB
MySQL
PostgreSQL
Prisma
SQLite
Lemon Squeezy
Paddle
Stripe
Next.js
React

Features:

Admin
AI
Analytics
Auth
Billing
Blog
Dark Mode
+12 more
Visit website for SaaSConstruct

SaaSConstruct

AWS cloud template for building SaaS applications in one day

JavaScript
Python
TypeScript
Vue.js
AWS
Lemon Squeezy
Stripe
AWS CDK
Vue.js

Features:

AI
API
Auth
AWS
Billing
Blog
CI/CD
+9 more
Visit website for Supastarter

Supastarter

Scalable and production-ready SaaS starter kit for Next.js, Nuxt, and SvelteKit.

JavaScript
TypeScript
Radix UI
Radix Vue
shadcn/ui
Tailwind CSS
Prisma
Chargebee
Creem
Lemon Squeezy
Polar
Stripe
Next.js
Nuxt
React
Svelte
SvelteKit
Vue.js

Features:

Access Control
AI
Analytics
API
Auth
Blog
Contact
+10 more
Visit website for Shipped

Shipped

The Next.js SaaS Boilerplate for busy developers

JavaScript
TypeScript
ChakraUI
shadcn/ui
Tailwind CSS
MongoDB
MySQL
PostgreSQL
Prisma
Lemon Squeezy
Stripe
Next.js
React

Features:

AI
Auth
Blog
Charts
Dashboard
Emails
Landing Page
+6 more

Showing 9 of 41 boilerplates

Why Choose AI Boilerplates?

AI represents a complete full-stack feature with dedicated API endpoints, database models, and UI components architected for SaaS applications. Our boilerplates with AI implement layered architecture patterns—separating business logic, data access, and presentation—with security measures and testing strategies specific to AI's functionality.

AI boilerplates implement full-stack architecture with service layers for business logic, repository patterns for data access, and RESTful/GraphQL API endpoints. They include AI-specific security measures like input validation with schema libraries (Zod, Joi), parameterized queries for SQL injection prevention, and CSRF protection. The implementation handles AI's real-time requirements with WebSockets or SSE when needed, includes comprehensive error handling, and follows OWASP security guidelines for AI's functionality.

Key Benefits

  • AI layered architecture
  • AI-specific security measures
  • AI API endpoint design
  • AI real-time capabilities
  • AI validation schemas
  • AI error handling
  • AI testing suite
  • AI performance optimization

Browse our collection of 41 AI boilerplates to find the perfect starting point for your next SaaS project. Each boilerplate has been carefully reviewed to ensure quality, security, and production-readiness.

Frequently Asked Questions

How is AI architecturally implemented?

AI is implemented following full-stack architecture patterns with dedicated API endpoints, database models with proper relationships, and corresponding UI components. The feature includes its own service layer for business logic, validation schemas, error handling, and event-driven updates. The architecture separates concerns between presentation, business logic, and data access layers, making AI maintainable and testable.

What security measures protect AI?

AI implements defense-in-depth security including input validation with schema validation libraries (Zod, Joi, Yup), parameterized database queries to prevent SQL injection, output encoding to prevent XSS attacks, CSRF token validation, and proper authentication/authorization checks. The feature includes rate limiting, audit logging, and follows OWASP security guidelines specific to AI's functionality.

How does AI handle real-time updates?

AI can include real-time capabilities using WebSockets, Server-Sent Events (SSE), or polling strategies depending on the use case. Real-time implementations use Socket.io, native WebSockets, or framework-specific solutions with proper connection management, authentication, and scaling considerations. The feature handles reconnection logic, message queuing, and optimistic UI updates for responsive user experience.

What API patterns does AI use?

AI's API endpoints follow RESTful principles or GraphQL patterns with proper HTTP methods, status codes, and response structures. The implementation includes request validation, pagination for list endpoints, filtering and sorting capabilities, and comprehensive error responses with meaningful messages. API versioning, rate limiting per endpoint, and OpenAPI/GraphQL schema documentation are included for AI's public-facing endpoints.

How is AI tested and validated?

AI includes unit tests for business logic, integration tests for API endpoints and database interactions, and end-to-end tests for critical user flows. The testing suite uses framework-specific tools (Jest, Pytest, RSpec, PHPUnit) with mocking libraries, test fixtures, and database seeding. Tests cover happy paths, error cases, edge conditions, and security scenarios specific to AI's functionality with proper test coverage reporting.